Here’s the README which describes how to execute this project

# ByteCode Project

This project integrates machine learning models with a Flask-based web interface. It includes modules for general predictions using an LSTM model and a crop recommendation system.

## Table of Contents
- [Project Setup](#project-setup)
- [Installation](#installation)
- [Usage Instructions](#usage-instructions)
  - [Running the Main Prediction Model](#running-the-main-prediction-model)
  - [Running the Crop Recommendation Module](#running-the-crop-recommendation-module)
- [Technical Overview](#technical-overview)
- [Contributing](#contributing)
- [License](#license)

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## Project Setup

1. **Clone the Repository**  
   Fork this repository or clone it directly:
   ```bash
   git clone https://github.com/gourab9817/ByteCode.git

2. Install Dependencies
   Navigate to the project directory and install all dependencies:

   pip install -r requirements.txt

3. Configure Data Paths

   • The ByteCode directory contains all data files, training and testing scripts, and Flask applications.
   • Update the path for dataset.csv in your code to match your local directory structure.

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Usage Instructions

Running the Main Prediction Model

1. Navigate to the flask_app Directory
   Change to the main Flask app directory:

   cd ByteCode/flask_app

2. Start the Flask Application
   Run the following command to start the main Flask application:

   python app.py

   This will launch a server and display a route link in the terminal. Copy and paste this link into your web browser to access the application.

3. Using the Web Interface

   • On the provided route link, you can enter values to get predictions from the trained LSTM model.

Running the Crop Recommendation Module

1. Open a New Terminal
   To start the crop recommendation module, open a new terminal window.

2. Navigate to the Crop Recommendation Directory
   Change to the crop_recommendation directory:

   cd ByteCode/crop_recommendation

3. Start the Crop Recommendation Script
   Run the crop recommendation module:

   python crop_recommendation.py

   This module will also provide a route link. Use this link to access the crop recommendation web interface, where you can enter values for crop prediction and view results.

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Technical Overview

• Main Application (flask_app): Hosts the primary prediction model and web interface.
• Models: Utilizes an LSTM model trained on datasets in the ByteCode directory.
• Crop Recommendation Module: Provides crop-specific predictions based on various user inputs.

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🤝 Contribution Guidelines

To contribute to this project:

1. Fork the repository and create a feature branch:

   git checkout -b feature/YourFeature

2. Commit your changes and push to your branch:

   git commit -m "Add feature description"
   git push origin feature/YourFeature

3. Open a pull request for review.

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License

Distributed under the MIT License. See LICENSE for more information.

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This `.md` formatted file will render well on GitHub and provide clear instructions for setting up, using, and contributing to the ByteCode project.